Evaluating biological plausibility of learning algorithms the lazy wayDownload PDF

11 Sept 2019, 14:22 (modified: 08 Jul 2022, 17:26)Real Neurons & Hidden Units @ NeurIPS 2019 PosterReaders: Everyone
TL;DR: We evaluate new ML learning algorithms' biological plausibility in the abstract based on mathematical operations needed
Keywords: Machine learning, back propagation through time, biological plausibility, online learning
Abstract: To which extent can successful machine learning inform our understanding of biological learning? One popular avenue of inquiry in recent years has been to directly map such algorithms into a realistic circuit implementation. Here we focus on learning in recurrent networks and investigate a range of learning algorithms. Our approach decomposes them into their computational building blocks and discusses their abstract potential as biological operations. This alternative strategy provides a “lazy” but principled way of evaluating ML ideas in terms of their biological plausibility
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